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54 results for “Implementation Plan”
Fig. 8 in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 8. Fitted linear model of the relationship between Actions' implementation before and after BAP.
Fig. 7 in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 7. Box-and-Whicker plots for Species Conservation and Habitats Conservation Sub-actions' implementation in points before and after BAP.
Data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>This data set contains raw data and parsed data of all experiments [1] run for the PhD thesis. They were generated using lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>The raw data files (sievers-phd2017-raw-data-part*.tar.gz) contain a subdirectory for each experiment, each containing a subdirectory for each planner run of the experiment, distributed over the directories runs-*. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), and the run script "run" used to start the experiment. The latter cannot be directly used, however, because the directory containing source code and build (compiled object files) have been removed for space reasons. The code is publicly available under https://doi.org/10.5281/zenodo.1163381. The (lab) scripts for parsing run.log are also available in the main directory of each experiment. All other scripts and a corresponding lab version are available on request.</p> <p>For each raw data experiment, the parsed data file (sievers-phd2017-parsed-data.tar.gz) also contains a directory of the same name, with "-eval" appended. It contains a single file called "properties" that combines all of the experiment's parsed data (which can be and was generated from the raw data using lab and the parser scripts). They are in the json format and can be used for easy manipulation of the data. The directories with the prefix "paper-" and "talk-" are combinations of other directories (using the "fetch" mechanism of lab). It is recommended to use these, because due to technical errors, the original eval directories do not contain all runs of all planners (to be more precise: they contain all runs, but a subset of the planner have not been started in these experiments for technical errors and thus considered not solving the task). The missing ones have been run separately, see the directories with "missing-runs" in their name. This is also the reason some of these directories ("paper-", "talk-") contain files named "old-properties" and "fixed-properties" besides the actual "properties". "old-properties" are those with missing/faulty runs, "fixed-properties" are as "old-properties", however with the data of faulty runs removed, and "properties" are as "fixed-properties", however with the addition of the fixed missing runs (in fact, these always contain *all* fixed missing runs of all experiments, for technical reasons).</p> <p>The file sievers-phd2017-parsed-data-all-and-random-merge-strategies.tar.gz contains parsed data of earlier experiments (see [1]), for which no raw data has been archived. The directories contain properties files in the json format.</p> <p>[1] except raw data for the parsed data "sota-symba-spmas-eval" (which in the meantime was added to a separate data set available under https://doi.org/10.5281/zenodo.1189912) and all re-used experiments from the paper "An Analysis of Merge Strategies for Merge-and-Shrink Heuristics" (Silvan Sievers, Martin Wehrle and Malte Helmert, ICAPS 2016), for which the raw data was too large to be archived.</p>
Dataset used in the paper "Merge-and-Shrink Heuristics for Classical Planning: Efficient Implementation and Partial Abstractions"
<p>This dataset contains all raw and processed data used in the paper. It has been generated using Downward-Lab (see https://doi.org/10.5281/zenodo.399255).</p> <p>Directories without the "-eval" ending contain raw data, distributed over a subdirectory for each experiment. Each of these contain a subdirectory tree structure "runs-*" where each planner run has its own directory. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), the run script "run" used to start the experiment, and a "properties" file that contains data parsed from the log file(s).</p> <p>Directories with the "-eval" ending contain a "properties" file, which contains a JSON directory with combined data of all runs of the corresponding experiment. In essence, the properties file is the union over all properties files generated for each individual planner run.</p> <p>To process the data further, we used the scripts available in the software bundle of the paper: https://doi.org/10.5281/zenodo.1290524</p>
Guidelines for Data Management Plan implementation of One Health EJP projects: Webinar held on the 19th December 2018
<p>Webinar held on the 19th December 2018 to introduce Data Management Plan to scientists involved in the research and integrative projects of One Health European Joint Program.</p>
CAP1 - Planning and control of asphalt production - Planning algorithm implemented in R
<p>CAP1- Planning and Optimization of Asphalt Production</p> <p>The cognitive planning solution for asphalt production consists of a planning decision tool that, from the sensors data installed in the plant and contributing to the planning reference implementation layer of the CAP, allows to decide the right moment to start the production and the necessary adjustments to get the asphalt mix to leave the production plant to the asphalt application area in the optimal conditions (temperature mainly). It takes into account the industrial data streams coming from both the local control system located at the asphalt use case and the data coming from the new different sensors that have been connected to the local datalogger also available at the asphalt production plant and as part of this project development.</p> <p>The implementation of the cognitive system of production planning and optimization gathers all the data coming from the cognitive sensors developed in the project (as the content of bitumen or filler present in the asphalt mix) and any other sensors already installed alongside, with data coming from the laboratory if needed.</p> <p>It is needed to perform two types of calculations:</p> <ul> <li>A <strong>mass balance</strong> both at the dryer and mixing process on a daily basis and for each type of asphalt mix design (a recipe containing the proportions of each ingredient, the aggregates, bitumen and recycled asphalt).</li> <li>A <strong>thermal balance</strong> also both at the dryer (heating up the cold aggregates) and mixing of the hot aggregates, the bitumen and the cold RAP (recycled asphalt). Both processes have a temperature set point. For the drying, there is a temperature safety limit to not damage the baghouse filter. For the mixing, the temperature is set by the asphalt mix design so the final mix is transported and laid out at the job site at a minimum temperature.</li> </ul> <p>The advanced calculation of the mass balance throughout all the production chain is performed including the continuous part of it (aggregates drying process) and the batch one (mix tower). This mass balance is made up of the different calculations that can be performed using all the available data and taking into consideration both, stationary and dynamic (transitory) mass balances like mass balance of aggregates in the dryer, mass balance in the baghouse filter, mass balance in the bucket elevator to the mixing tower, mass balance in the upper sieves and in the hot aggregates hoppers and eventually the mass balance in the mixer taking into account the different additives (including RAP, bitumen, etc.). Also, the different recipes production historical data is used as a basis for the calculations of this tool.</p>
Aligning Data Management Plans with Community Standards using FAIR Implementation Profiles
<p>Here you can find the files corresponding to our submission titled 'Aligning DMPs with Community Standards using FIPs'.</p> <p>- VU DMP template and the mapping is included in the folder /VU-DMP-template-and-mapping</p> <p>- All the FAIR Implementation Profiles are included in the folder /FIPs.</p> <p>- The knowledge model we created for the project, and a small demo of the interface are in the folder /KM-and-demo.</p> <p>- The folder /user-study consists of the following:</p> <p> a) The mock DMPs we provided to the participants of this research are in /mock_DMPs.</p> <p> b) We downloaded the resulting DMPs after participants completed their DMPs, they are in the folder /resulting_DMPs.</p> <p> c) Survey results can be found in the folder /survey_results.</p> <p> d) Some Python scripts were used for the analysis of the survey results. They are in the folder /Python_script_for_analysis.</p> <p><br>The project is open source under the license CC-BY 4.0.</p> <p>Contact: Shuai Wang (shuai.wang@vu.nl)</p> <p> </p>
Fig. 5 in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 5. Invasive fish Perccottus glenii from pond of B.bombina (Ainavas).
Fig. 6. B in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 6. B.bombina populations found in Latvia before and after BAP.
Fig. 1. B in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 1. B.bombina tadpoles in aquaculture in the Latgales Zoo.
Fig.3 in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig.3. Two new micro-reserves Strauti and Katriniski, established for B.bombina after BAP.
Fig. 4. Optimized pond for B in Action Plan For The Fire-Bellied Toad Bombina Bombina In Latvia: Assessment Of The Implementation For Ten Years, Releasing From Aquaculture And Restoration Of Habitats In 2006-2016
Fig. 4. Optimized pond for B.bombina population in Katriniski.
Fig. 3 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 3. Map of the landscape terrain at Nature park "Dridža ezers" ("Lake Dridža")
Fig. 1 in The Implementation Of Nature Management Plans For Specially Protected Nature Territories In Latvia: Stakeholder Awareness, Applying Gis Tools, Indicators For Management
Fig. 1. Interactive map of recreation sites at Nature park "Dridža ezers" ("Lake Dridža")
Additional data of the PhD thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation"
<p>The original data set for the thesis "Merge-and-Shrink Abstractions for Classical Planning: Theory, Strategies, and Implementation" by Sievers, 2017, available under https://doi.org/10.5281/zenodo.1164137, accidentally did not include the data of one experiment, namely the raw data of the parsed data contained in "sota-symba-spmas-eval". It can be found in this data set.</p>
Feedback on the draft implementation guidance of Plan S
<p>Launched in September 2018, cOAlition S is an initiative of an international consortium of research funders to make full and immediate Open Access to research publications a reality. It is built around Plan S, which consists of one target and 10 principles. In November 2018, cOAlition S released a draft guidance on the implementation of Plan S for public feedback. This process, which lasted from November 2018 to February 2019, collected more than 600 feedback statements from universities, learned societies, publishers, scholarly associations, and individual scholars from more than 40 countries. The documents include all responses and accompanying statements received during the process. Responses have been analysed and an updated guidance on implementation was adopted and published in May 2019. In order to facilitate the download of the full results, a Zip file (002_Plan S_Feedback_all_files) has been added in version 3. Please visit cOAlition S website for more information: <a href="https://www.coalition-s.org/">https://www.coalition-s.org/</a></p>
Effectiveness of the Implementation of a Standardized Care Plan to Improve Fear of Falling and Incidence of Falls
ClinicalTrials.gov study NCT05889910. IPD Sharing: YES. Countries: 1. Publications: 25.
Implementing Health Plan-Level Care Management for Solo & Small Practices
ClinicalTrials.gov study NCT02041962. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Implementing an Individualized Pain Plan (IPP) for ED Treatment of VOE's in Sickle Cell Disease
ClinicalTrials.gov study NCT04584528. IPD Sharing: YES. Countries: 1. Publications: 2.
Implementation Effectiveness and Safety of Tenofovir Gel Provision Through Family Planning Services
ClinicalTrials.gov study NCT01691768. IPD Sharing: Not stated. Countries: 1. Publications: 3.
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